By Ajay Lohany
Urban Air Mobility (UAM) is at the forefront of transforming urban transportation, utilizing electric vertical take-off and landing (eVTOL) aircraft for passenger and cargo transport. This revolutionary approach promises a faster, more efficient, sustainable, quieter, and environmentally friendly alternative to traditional transportation. As the concept takes shape, various Original Equipment Manufacturers (OEMs) globally are already engaged in the development of diverse eVTOL aircraft designs. The global urban air mobility market size was USD 5.83 Billion in 2022 and is expected to reach USD 23.42 Billion in 2032.
Entering a New Era
With the integration of advanced technologies such as 5G, hyper-automation, AI/ML, and analytics in every aspect of life, the UAM domain is poised for significant transformation. Similar to other modes of transportation, UAM vehicles will require regular maintenance to ensure safety and reliability. Artificial Intelligence (AI) is expected to play a pivotal role in reshaping UAM maintenance, offering benefits and tackling associated challenges.
AI in UAM Maintenance
AI can be applied in multiple ways to enhance UAM maintenance:
- Predictive Maintenance: AI algorithms can analyze data from sensors and onboard sources to predict when UAM vehicles will require maintenance. This proactive approach allows maintenance teams to schedule tasks efficiently, minimizing downtime and improving vehicle availability.
- Maintenance Inspections: AI can analyze images and data collected during inspections to swiftly identify potential issues, aiding maintenance teams in prompt and accurate issue resolution. This reduces the risk of errors and oversights.
- Decision Support: AI can process complex data, including repair costs, spare parts availability, and impact on vehicle availability, providing maintenance teams with informed recommendations for decision-making.
- Autonomous Maintenance Systems: Future AI-powered autonomous maintenance systems could perform certain tasks without human intervention. Robotic systems equipped with AI algorithms might conduct routine inspections, minor repairs, or component replacements, thereby enhancing efficiency and safety.
- Diagnostics and Troubleshooting: In case of issues, AI can assist technicians in diagnosing problems by analyzing sensor data, historical maintenance data, and technical manuals. This helps identify the root cause and suggests appropriate repair actions.
Operational Safety and Efficiency
The application of AI in UAM maintenance offers several benefits:
- Proactive Maintenance: AI enables predictive maintenance by analyzing large volumes of data, identifying potential failures before they occur, reducing unplanned downtime, and enhancing operational reliability.
- Increased Aircraft Availability: AI-driven maintenance support improves vehicle availability, reduces downtime, and ensures safety, contributing to increased customer satisfaction and overall operational performance.
- Cost Reduction: AI optimization of maintenance processes helps operators minimize unnecessary inspections and component replacements, reducing labor and material costs, and maximizing revenue generation.
- Improved Safety: Continuous monitoring of UAM vehicle conditions by AI systems can detect anomalies or potential safety risks in real-time, preventing accidents and ensuring timely resolution of maintenance issues.
Data and Integration Challenges
Despite the benefits, challenges must be addressed, including data quality and availability. AI algorithms rely on substantial, high-quality data for accurate predictions. Additionally, integrating AI with existing maintenance processes poses a challenge, as operators need to navigate disruptive changes. Regulatory challenges may also arise, necessitating collaboration between UAM operators, regulators, and stakeholders to develop effective rules and guidelines for safe AI usage in UAM maintenance.
Evolving Flight Path
The incorporation of AI in UAM maintenance has the potential to revolutionize aircraft maintenance practices. Through predictive maintenance, improved inspection accuracy, and enhanced decision-making support, AI can contribute to the safety, reliability, and availability of UAM vehicles. While challenges exist, the significant benefits of AI in UAM maintenance suggest ongoing progress in this domain.
Today, the evolution of AI and ML technology only opens up greater opportunities to enable the UAM market to improve operations, transform their business and deliver superior products and services in the market.
About the author
Ajay Kumar Lohany, Delivery Sr. Director- Aero & Rail, Cyient, is an aeronautical engineer with specialization in avionics systems. He holds a master’s degree in computer science and in Modeling and Simulation. He has served in the Indian Air Force as a flight test and instrumentation engineer and has 32+ years of industry experience. He takes keen interest in building technological solutions that help solve problems in the aerospace and rail domains, and the views expressed in this article are his own
